Authentication in distributed systems: theory and practice
ACM Transactions on Computer Systems (TOCS)
Practical multi-candidate election system
Proceedings of the twentieth annual ACM symposium on Principles of distributed computing
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Information sharing across private databases
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
TAILOR: A Record Linkage Tool Box
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Privacy-preserving data integration and sharing
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Top-Down Specialization for Information and Privacy Preservation
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Blocking-aware private record linkage
Proceedings of the 2nd international workshop on Information quality in information systems
Mondrian Multidimensional K-Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Privacy Preserving Query Processing Using Third Parties
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
PRIVATE-IYE: A Framework for Privacy Preserving Data Integration
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Privacy preserving schema and data matching
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Hiding the presence of individuals from shared databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Building application-related patient identifiers: what solution for a European country?
International Journal of Telemedicine and Applications - Pervasive Health Care Services and Technologies
A Privacy-Preserving Framework for Integrating Person-Specific Databases
PSD '08 Proceedings of the UNESCO Chair in data privacy international conference on Privacy in Statistical Databases
HIDE: An Integrated System for Health Information DE-identification
CBMS '08 Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems
A Hybrid Approach to Private Record Linkage
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Public-key cryptosystems based on composite degree residuosity classes
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
Privacy-preserving set operations
CRYPTO'05 Proceedings of the 25th annual international conference on Advances in Cryptology
Performance-oriented privacy-preserving data integration
DILS'05 Proceedings of the Second international conference on Data Integration in the Life Sciences
Private itemset support counting
ICICS'05 Proceedings of the 7th international conference on Information and Communications Security
A Cryptographic Approach to Securely Share and Query Genomic Sequences
IEEE Transactions on Information Technology in Biomedicine
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Organizations, such as federally-funded medical research centers, must share de-identified data on their consumers to publicly accessible repositories to adhere to regulatory requirements. Many repositories are managed by third-parties and it is often unknown if records received from disparate organizations correspond to the same individual. Failure to resolve this issue can lead to biased (e.g., double counting of identical records) and underpowered (e.g., unlinked records of different data types) investigations. In this paper, we present a secure multiparty computation protocol that enables record joins via consumers' encrypted identifiers. Our solution is more practical than prior secure join models in that data holders need to interact with the third party one time per data submission. Though technically feasible, the speed of the basic protocol scales quadratically with the number of records. Thus, we introduce an extended version of our protocol in which data holders append k-anonymous features of their consumers to their encrypted submissions. These features facilitate a more efficient join computation, while providing a formal guarantee that each record is linkable to no less than k individuals in the union of all organizations' consumers. Beyond a theoretical treatment of the problem, we provide an extensive experimental investigation with data derived from the US Census to illustrate the significant gains in efficiency such an approach can achieve.